This project responds directly to the solicitation for LCLUC studies in mountainous regions by examining forest dynamics in Nepal since the early 1990s. The project has three major objectives: 1) Build a comprehensive database of forest cover change in the Middle Hills over the last 25 years 2) Identify the socioeconomic and physiographic variables associated with forest cover change and quantify their respective influences and 3) Assess how foreign labor migration and remittances correlate to forest cover change across the Middle Hills and at a sample of community forest sites. An annual time series of Landsat TM, ETM+, and OLI imagery will be the backbone of the forest cover change analysis. Landsat imagery will be corrected to at-surface reflectance and the input to an illumination correction model with 30 m spatial resolution ASTER Global Digital Elevation Model Version 2 (GDEM2) topographic elevation data to mitigate terrain-driven conditions of illumination. Intra-annual topographically-corrected imagery will be composited to fill cloud and ETM+ Scan Line Corrector- error gaps if necessary. Resulting composites will be input to the Vegetation Change Tracker (VCT) forest cover disturbance detection algorithm to map inter -annual forest cover disturbance and regrowth. Extensive field work will be required to collect ground truth for validating Landsat imagery-derived change. In regions of relative inaccessibility, forest cover change as visually interpreted in very high resolution satellite imagery will provide validation data to complement on-the-ground data collection for the accuracy assessment.

We will examine quantitative relationships between socioeconomic and physiographic variables and conditions of successful forest management utilizing a variety of data including ASTER GDEM2 topographic data, soil type from the Soil and Terrain (SOTER) database, and infrastructural data from OpenStreetMap. Data on socioeconomic variables will be gathered from national censuses that we will acquire, in collaboration with our Nepali partners, from the Central Bureau of Statistics. Independent socioeconomic variables and physiographic variables will be summarized at district and VDC levels. Their respective influences on the dependent variables of forest cover change (e.g., extent, rate of disturbance, rate of recovery, etc.) will be input to a regression forest model. A Middle Hills-wide regression forest relating socioeconomic and physiographic variables to forest cover change will, first, be used to identify the most significant district and VDC level predictors of forest cover loss or regrowth, the latter being a proxy for successful forest management and, second, be applied across the Middle Hills to model the type (i.e., gain or loss) and amount (i.e., rate or extent) of forest cover change expected given a region’s socioeconomic and physiographic conditions. Finally, to determine if there are statistically significant relationships between forest cover change and migration and remittances, we will compare Landsat-derived forest cover change data to the spatial distribution of remittance income from migrants working abroad. We will then examine these relationships in greater detail at a sample of forests (at least 10) that have shown regrowth or stagnation between 1990 and the present. The project’s significance to NASA lies in its improved methods for mapping forest cover in mountainous regions, and its integration of methods and datasets for conducting a comprehensive, interdisciplinary assessment of LCLUC in mountainous regions. Furthermore, the project seeks to understand LCLUC processes in the Middle Hills in a way that not only produces insights into the social and ecological transformations of the region but also broadly advances forest transition theory.